Hi everyone,
as part of our ongoing development at BlockAI, we are currently working on a new version of an AI-driven DAO. The underlying concept is based on a structured decision-making model known as QOC (Question–Option–Criteria).
We have previously outlined the general idea in a Medium article:
https://blockai.medium.com/qoc-a-new-approach-to-make-daos-more-transparent-and-fair-24821e0c4e71
In the meantime, we have also evaluated this approach scientifically. A preprint of the corresponding paper is available on arXiv:
https://arxiv.org/abs/2511.08641
In the paper, we propose a stepwise introduction of the QOC-based DAO model. Together with Sasha, we have agreed to start with step 2: the AI evaluates and decides on proposals, but its decision is not executed automatically. Instead, the final decision remains with the community via a vote. In this setup, the AI’s output should be understood as a recommendation rather than a binding action.
This has two intended benefits:
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It acts as a safety mechanism to verify that the AI behaves as expected.
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It allows the community to gradually become familiar with the new decision-making process in a transparent and controllable way.
Within this context, we are currently facing a conceptual challenge. Since AI decisions are not executed directly, but are followed by a community vote, four possible outcome combinations emerge.
If the AI approves a proposal, the situation is straightforward:
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If the community also approves, the proposal is executed.
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If the community rejects it, the proposal is not executed.
The situation becomes less clear when the AI rejects a proposal. This raises two key questions:
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Should a proposal that is rejected by the AI still be put to a community vote?
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If the community votes in favor of such a proposal, should it be executed anyway, or should execution require a positive AI recommendation as well?
We would very much appreciate hearing your perspectives and experiences on how to handle these cases.
Best regards,
Christophe & Marc